Advancements and challenges of digital twins in industry

Research and Prospects of Digital Twin-Based Fault Diagnosis of Electric Machines

This paper focuses on the application of digital twins in the field of electric motor fault diagnosis. Firstly, it explains the origin, concept, key technology and application areas of digital twins, compares and analyzes the advantages and disadvantages of digital twin technology and traditional methods in the application of electric motor fault diagnosis, discusses in depth the key technology of digital twins in electric motor fault diagnosis, including data acquisition and processing, digital modeling, data analysis and mining, visualization technology, etc., and enumerates digital twin application examples in the fields of induction motors, permanent magnet synchronous motors, wind turbines and other motor fields. A concept of multi-phase synchronous generator fault diagnosis based on digital twins is given, and challenges and future development directions are discussed.

Evaluation framework for domain-specific digital twin platforms

As digital twin (DT) applications continue to proliferate across diverse industries, a noticeable gap exists in the availability of evaluation methods or frameworks to aid in the selection and development of DT platforms, particularly for Domain-Specific applications. To address this gap, this paper proposes a comprehensive evaluation framework for DT platforms, with a focus on Domain-Specific applications. The framework uses the Best-Worst Method and Fuzzy Comprehensive Evaluation method (BWM-FCE) to assess the performance, user experience, and economic effects of DT platforms. The proposed framework is applied to a case study of a high-speed train DT platform and compared with other evaluation methods AHP(Analytic Hierarchy Process) and BWM-SPA (Best-Worst Method-Set Pair Analysis). The results demonstrate the feasibility and effectiveness of the proposed framework and highlight its potential for guiding the development and selection of DT platforms for Domain-Specific applications.

Concept and framework of digital twin human geographical environment

The human geographical environment is a comprehensive setting formed by the interaction between human activities and the geographical environment, characterized by its complexity and vulnerability. Applying the digital twin method to create a new research model in a human geographical environment holds significant academic and practical value. This approach helps avoid disturbances in the real environment, deeply explores complex issues, and optimizes solutions for real-world geographical problems. By reviewing the current state of research, we propose the basic concept of the digital twin human geographical environment and elaborate on its meaning. Additionally, we construct a technical framework for the digital twin human-geographical environment system. We identify the digital twin human-geographical environment as comprising the real human geographical environment, the virtual human geographical environment, and the interaction between the two. This is achieved through the twin construction and interaction of the geographical environment, human activities, and human geographical interaction, facilitating coordination and mutual enhancement between the real and virtual environments. Using the digital twin of Bailudong Academy as a case study, we demonstrate the construction methods, main functions, and result forms of a digital twin human geographical environment. Besides, this paper will provoke thoughts on the coupling of digital twins and human geographical environments, jointly promoting the development of human geography.

Elevator fault diagnosis based on digital twin and PINNs-e-RGCN

The rapid development of urbanization has led to a continuous rise in number of elevators. This has led to elevator failures from time to time. At present, although there are some studies on elevator fault diagnosis, they are more or less limited by the lack of data to make the research more superficial. For such complex special equipment as elevator, it is difficult to obtain reliable and sufficient data to train the fault diagnosis model. To address this issue, this paper first establishes a numerical model of vertical vibration for elevators with three degrees of freedom. The obtained motion equations are then used as constraints to acquire simulated vibration data through PINNs. Next, the proposed e-RGCN is employed for elevator fault diagnosis. Finally, experimental validation shows that the fault diagnosis accuracy with the participation of digital twins exceeds 90%, and the accuracy of the proposed model reaches 96.61%, significantly higher than that of other comparative models.

Digital twins in mechanical and aerospace engineering

Digital twins bring value to mechanical and aerospace systems by speeding up development, reducing risk, predicting issues and reducing sustainment costs. Realizing these benefits at scale requires a structured and intentional approach to digital twin conception, design, development, operation and sustainment. To bring maximal value, a digital twin does not need to be an exquisite virtual replica but instead must be envisioned to be fit for purpose, where the determination of fitness depends on the capability needs and the cost-benefit trade-offs.